Saturday, 23 December 2017

2018 IEEE Congress on Evolutionary Computation (IEEE
CEC)

Overview

The Brain Storm Optimization (BSO)
algorithm is a new kind of swarm intelligence algorithm, which is based on the
collective behaviour of human being, that is, the brainstorming process. There
are two major operations involved in BSO, i.e., convergent operation and
divergent operation. A ``good enough'' optimum could be obtained through
recursive solution divergence and convergence in the search space. The designed
optimization algorithm will naturally have the capability of both convergence
and divergence.

BSO possess two kinds of functionalities:
capability learning and capacity developing. The divergent operation
corresponds to the capability learning while the convergent operation
corresponds to capacity developing. The capacity developing focuses on moving
the algorithm's search to the area(s) where higher potential solutions may
exist while the capability learning focuses on its actual search towards new
solution(s) from the current solution for single point based optimization
algorithms and from the current population of solutions for population-based
swarm intelligence algorithms. The capability learning and capacity developing
recycle to move individuals towards better and better solutions. The BSO
algorithm, therefore, can also be called as a developmental brain storm
optimization algorithm.

The capacity developing is a top-level
learning or macro-level learning methodology. The capacity developing describes
the learning ability of an algorithm to adaptively change its parameters,
structures, and/or its learning potential according to the search states of the
problem to be solved. In other words, the capacity developing is the search
potential possessed by an algorithm. The capability learning is a bottom-level
learning or micro-level learning. The capability learning describes the ability
for an algorithm to find better solution(s) from current solution(s) with the
learning capacity it possesses.

The BSO algorithm can also be seen as a
combination of swarm intelligence and data mining techniques. Every individual
in the brain storm optimization algorithm is not only a solution to the problem
to be optimized, but also a data point to reveal the landscapes of the problem.
The swarm intelligence and data mining techniques can be combined to produce
benefits above and beyond what either method could achieve alone.

Topics of Interest

This special session aims at presenting the
latest developments of BSO algorithm, as well as exchanging new ideas and
discussing the future directions of developmental swarm intelligence. Original
contributions that provide novel theories, frameworks, and applications to
algorithms are very welcome for this Special session.

Data mining, machine learning, and optimisation algorithms have achieved promises in many real-world tasks, such as classification, clustering and regression. These algorithms can often generalise well on data in the same domain, i.e. drawn from the same feature space and with the same distribution. However, in many real-world applications, the available data are often from different domains. For example, we may need to perform classification in one target domain, but only have sufficient training data in another (source) domain, which may be in a different feature space or follow a different data distribution. Transfer learning aims to transfer knowledge acquired in one problem domain, i.e. the source domain, onto another domain, i.e. the target domain. Transfer learning has recently emerged as a new learning framework and hot topic in data mining and machine learning.

Aim and Scope:

Evolutionary computation techniques have been successfully applied to many real-world problems, and started to be used to solve transfer learning tasks. Meanwhile, transfer learning has attracted increasing attention from many disciplines, and has been used in evolutionary computation to address complex and challenging issues. The theme of this special session is transfer learning in evolutionary computation, covering ALL different evolutionary computation paradigms, including Genetic algorithms (GAs), Genetic programming (GP), Evolutionary programming (EP), Evolution strategies (ES), Learning classifier systems (LCS), Particle swarm optimization (PSO), Ant colony optimization (ACO), Differential evolution (DE), Evolutionary Multi-objective optimization (EMO) and Memetic computing (MC).

The aim is to investigate in both the new theories and methods on how transfer learning can be achieved with different evolutionary computation paradigms, and how transfer learning can be adopted in evolutionary computation, and the applications of evolutionary computation and transfer learning in real-world problems.

Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to:

Important dates:

Deadline for submission of full papers: 15 January 2018

Notification of acceptance: 15 March 2018

Deadline for camera-ready submission: 1 May 2018

Conference dates: 8-13 July, 2018

Paper Submission:

Please follow the IEEE WCCI/CEC2018 Submission Web Site. Special session papers are treated the same as regular conference papers. Please specify that your paper is submitted to SS08 Transfer Learning in Evolutionary Computation. All papers accepted and presented at WCCI/CEC2018 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.

Fireworks Algorithm (FWA) has become one of the promising swarm intelligence algorithms in recent years, and received extensive attentions from many researchers and practitioners, because it has shown a great success in solving many complex optimization problems, especially for multi-modal optimization problems frequently happened in a lot of real-world applications. Compared with many current SI algorithms, FWA is of a new explosive search manner and probably has a fine structure of search in the solution space. As a result, it shows a strong capability of optimization computation in many optimization problems. Till to date, it has many effective variants and huge amount of successful applications. Furthermore, FWA is suitable for parallelization and works significantly better than other SI algorithms.

The aim of this special session is to bring together the experts, active researchers and newcomers from either academia or industry over the world to discuss some important issues of fireworks algorithm and its progress. All of the latest work and achievements related FWA are all welcome to this special session under the umbrella of the IEEE Congress of Evolutionary Computation at the IEEE WCCI-2018.

Topics:

Full papers are invited on recent advances in the development of FWA, i.e., FWA improvements and applications. The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:

Computational Intelligence (CI), Artificial Intelligence (AI), Data Science and their applications are research areas jointly aligned to benefit research community and society. AI and Data Science encompass a broad field of Computational Intelligence disciplines including data mining, machine learning, ensemble learning, deep learning, fuzzy systems, and evolutionary computation, self-organizing systems and expert systems. In recognition of the escalating importance and relevance of examining the processes and results associated with obtaining and managing data, as well as scrubbing, exploring, modelling, interpreting, communicating and visualising data across all research domains, including Health, Education, Environment, Medicine, Security, Science, Technology, Business, the Humanities and the Arts, the aim of this Special Session is to allow researchers to communicate their high quality, original ideas by presenting and publishing new advances in computational intelligence to data science, engineering, internet of everything, internet of urgent things and their applications.

The world is moving through the fourth industrial revolution, which is happening all around us and affecting and changing the way we live, work and communicate with each other and the other devices around us. Widely used a new generation of artificial intelligence in intelligent medicine, smart city, robotics, intelligent manufacturing, intelligent energy, national defence and other fields will increase the core of computation intelligence and AI industry scale within the next decade. This session is dedicated to researchers and practitioners interested in strategies, theories, practices and tools, exchanging new theoretical, technical and experimental design. It focuses on CI and AI real-world applications and different use cases of solid findings and insights, best practices and applications to real-life situations, and reviewing new opportunities and frameworks for Data Sciences. This special session brings together CI, AI researchers and practitioners from different scientific disciplines with the goal of fostering collaboration between different and research groups. We aim to increase the understanding and use of AI techniques in the application to real world problems. We welcome contributions that deal with all aspects of the scientific foundations, theories, techniques and applications of computing, data and analytics, including but not limited to:

o Internet of Everything and Evolutionary computation

o AI Techniques Applied to Environmental Sciences

o Internet of Urgent things and its applications

o AI Techniques in Support of Aviation and Aerospace Op-erations

o Intelligent Approaches for Internet of Everything and its application

Prospective authors are invited to submit full-length papers (not exceeding 8 pages) by 15th January 2018. Submitted papers should conform to the IEEE format and will be handled and processed electronically via the IEEE CEC 2018 online submission system. Submission implies the willingness of at least one of the authors to register and present their paper. Further details can be found at http://dese.org.uk/IEEE-wcci/

You are invited to submit papers for the 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2018) to be held in Ottawa,Ontario, Canada from June 12-14, 2018. The conference is dedicated to all aspects of computational intelligence, virtual environments and human-computer interaction technologies for measurement systems and related applications.

2nd Special Session on Evolutionary Multi-objective Optimization based on Decomposition @ IEEE-WCCI/CEC 2018

8-13 July 2018 – IEEE WCCI 2018, Rio de Janeiro, Brazil

*** Scope

The purpose of this special session is to promote the design, study, and validation of generic approaches for solving multi­-objective optimization problems based on the concept of decomposition. Decomposition-based Evolutionary Multi-­objective Optimization (DEMO) encompasses any technique, concept or framework that takes inspiration from the "divide and conquer" paradigm, by essentially breaking a multi-­objective optimization problem into several sub­problems for which solutions for the original global problem are computed and aggregated in a cooperative manner. We encourage contributions reporting advances with respect to other decomposition techniques operating in the decision space or other hybrid approaches taking inspiration from operations research and mathematical programming. Many different DMOEAs variants have been proposed, studied and applied to various application domains. However, DEMOs are still in their very early infancy, since only a few basic design principles have been established compared to the huge body of literature dedicated to other well-established approaches (e.g. Pareto ranking, indicator-based techniques, etc). The main goal of the proposed session is to encourage research studies that systematically investigate the critical issues in DMOEAs at the aim of understanding their key ingredients and their main dynamics, as well a to develop solid and generic principles for designing them. The long-term goal is to contribute to the emergence of a general and unified methodology for the design, the tuning and the performance assessment of DEMOs.

*** Topics of interests

The topics of interests include (but are not limited to) the following issues:

Submission procedure, deadlines, and paper format are same than the IEEE-WCCI/CEC'18 main conference. In particular, we recall that papers must be submitted through the IEEE WCCI 2018 online submission system while selecting the ADEMO special session under the list of research topics in the submission system.

In their original versions, nature-inspired algorithms for optimization such as evolutionary algorithms (EAs) and swarm intelligence algorithms (SIAs) are designed to sample unconstrained search spaces. Therefore, a considerable amount of research has been dedicated to adapt them to deal with constrained search spaces. The objective of the session is to present the most recent advances in constrained optimization using different nature-inspired algorithms. The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:

Differential evolution (DE) emerged as a simple and powerful stochastic real-parameter optimizer more than two decades ago and has now developed into one of the most promising research areas in the field of evolutionary computation. The success of DE has been ubiquitously evidenced in various problem domains, e. g., continuous, combinatorial, mixed continuous-discrete, single-objective, multi-objective, constrained, large-scale, multimodal, dynamic and uncertain optimization problems. Furthermore, the remarkable efficacy of DE in real-world applications significantly boosts its popularity.

Over the past decades, numerous studies on DE have been carried out to improve the performance of DE, to give a theoretical explanation of the behavior of DE, to apply DE and its derivatives to solve various scientific and engineering problems, as demonstrated by a huge number of research publications on DE in the forms of monographs, edited volumes and archival articles. Consequently, DE related algorithms have frequently demonstrated superior performance in challenging tasks. It is worth noting that DE has always been one of the top performers in previous competitions held at the IEEE Congress on Evolutionary Computation. Nonetheless, the lack of systematic benchmarking of the DE related algorithms in different problem domains, the existence of many open problems in DE, and the emergence of new application areas call for an in-depth investigation of DE.

This special session aims at bringing together researchers and practitioners to review and re-analyze past achievements, to report and discuss latest advances, and to explore and propose future directions in this rapidly emerging research area. Authors are invited to submit their original and unpublished work in the areas including, but not limited to: